One of the reasons this channel is about decision making is that it's an incredibly broad topic that covers pretty much everything. But as much as I delve into this area, I find that there are some basics, and if those basics are not followed in the thought process, everything goes down the drain.
One of those fundamentals is this weird framework called the Cynefin Framework. I am pretty sure you can find the same or similar frameworks describing the same topic under other names, but whatever they are called, I find them fascinating to think about and apply.
Cynefin describes Contexts (or Domains) in which decisions are made. And it categorizes them by the degree of uncertainty (or predictability) in each situation. It does not stop there, but offers tools ⚒️ to act optimally in each of these areas.
There are 4 of them - Simple, Complicated, Complex and Chaotic. They are ordered according to the degree of predictability in each of these areas.
Simple Domain is where things are the most orderly and predictable, and most normal people can recognize cause-and-effect relationships without special preparation. This is the realm of what is known and best practice. Take a call center, for example - people know exactly what to do, they have predictable scripts and clear instructions, you do not have to train people for long, and if you are a manager there and you run into a problem, it's usually pretty easy to solve. Another example is any kind of assembly line - completely predictable, cause-and-effect are clear, everyone knows what to do.
Complicated Domain is a bit more complicated (😂 what a joke). Unlike in Simple domain, you have to an expect in this field to analyze and establish the cause-and-effect relationship. It is still predictable, but only for people who are extremely knowledgeable in that particular field. A good example is software development - to people who do not deal with it, it looks like magic, but to developers it is completely clear and predictable. This is also an area of best practices, but a bit more challenging.
Complex Domain. Ohh this is definitely my favorite area. Here, cause-and-effect relationships can only be identified after the fact. The information available to make decisions is limited, and there is no 100% guarantee of the outcome of a decision - you can only work with probabilities. There are no experts here, everyone learns as they go. A typical example of this field is innovative entrepreneurship. And the best approach is relentless experimentation, which gradually helps you establish causal relationships and move into the "complicated" area. I think this is the area where most real-life decisions are made.
Chaotic Domain. This is a scary area, the area of unknown unknowns, where nothing is predictable and causal relationships are not even apparent in hindsight. The stock market crash of 2008, for example, falls into this realm. A terrible war in Ukraine clearly falls into this realm. In this context, special leaders are needed who can "stop the bleeding," make really tough, directional decisions and order the chaos.
Each of these domains dictates different ways of dealing with it and making decisions when you are “in” it. For example, in the "Complex" domain, it's: Probe (experiment) → Sense → Respond (Decide). So your entire decision-making process relies on setting up the right experiments and learning from them.
In the simple domain, on the other hand, you just need to Sense (what's going on) → Categorize (using the MECE principle, of course 🙂) → React (or Decide).
As I pointed out in THIS ARTICLE, the whole innovation process is based on gradually reducing risk and making the process more predictable and controllable, which basically means moving from Chaotic or Complex domains to Simple domain. Because this is where the exits take place.
In Summary: